{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import pytorch_lightning as pl\n",
    "import numpy as np\n",
    "import time\n",
    "import scipy.io\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import scienceplots\n",
    "from DeepFlow import *\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(123456)\n",
    "np.random.seed(123456)\n",
    "device = torch.device('cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate Neural Network\n",
    "class DNN(pl.LightningModule):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(DNN, self).__init__()\n",
    "        self.net = nn.Sequential()                                                  # Define neural network\n",
    "        self.net.add_module('Linear_layer_1', nn.Linear(2, 30))                     # First linear layer\n",
    "        self.net.add_module('Tanh_layer_1', nn.Tanh())                              # First activation Layer\n",
    "\n",
    "        for num in range(2, 7):                                                     # Number of layers (2 through 7)\n",
    "            self.net.add_module('Linear_layer_%d' % (num), nn.Linear(30, 30))       # Linear layer\n",
    "            self.net.add_module('Tanh_layer_%d' % (num), nn.Tanh())                 # Activation Layer\n",
    "        self.net.add_module('Linear_layer_final', nn.Linear(30, 4))                 # Output Layer\n",
    "\n",
    "    # Forward Feed\n",
    "    def forward(self, x):\n",
    "        return self.net(x)\n",
    "\n",
    "    # Loss function for PDE\n",
    "    def loss_FVM(self, x, q ,nx,ny,idf):\n",
    "        y = self.net(x)\n",
    "        \n",
    "        q_1 = torch.tensor(q[1:ny-1,1:nx-1,0].flatten()[:, None],dtype=torch.float32).to(device)\n",
    "        q_2 = torch.tensor(q[1:ny-1,1:nx-1,1].flatten()[:, None], dtype=torch.float32).to(device)\n",
    "        q_3 = torch.tensor(q[1:ny-1,1:nx-1,2].flatten()[:, None], dtype=torch.float32).to(device)\n",
    "        q_4 = torch.tensor(q[1:ny-1,1:nx-1,3].flatten()[:, None], dtype=torch.float32).to(device)\n",
    "        \n",
    "        f = (((y[:,0:1] - q_1))**2).mean() + (((y[:,1:2] - q_2/q_1))**2).mean() + (((y[:,2:3] - q_3/q_1))**2).mean() + (((y[:,3:] - q_4/q_1))**2).mean()\n",
    "        \n",
    "        return f\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert torch tensor into np.array\n",
    "def to_numpy(input):\n",
    "    if isinstance(input, torch.Tensor):\n",
    "        return input.detach().cpu().numpy()\n",
    "    elif isinstance(input, np.ndarray):\n",
    "        return input\n",
    "    else:\n",
    "        raise TypeError('Unknown type of input, expected torch.Tensor or ' \\\n",
    "                        'np.ndarray, but got {}'.format(type(input)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Euler_IC(x,y,IC):\n",
    "    if IC == 1:\n",
    "        \n",
    "        p = [1.0,  0.4,     0.0439,  0.15  ];\n",
    "        r = [1.0,  0.5197,  0.1072,  0.2579];\n",
    "        u = [0.0, -0.7259, -0.7259,  0.0   ];\n",
    "        v = [0.0, -0.0,    -1.4045, -1.4045];\n",
    "        \n",
    "    if IC == 2:\n",
    "        \n",
    "        p = [1.0,  0.4,     1.0,     0.4   ];\n",
    "        r = [1.0,  0.5197,  1.0,     0.5197];\n",
    "        u = [0.0, -0.7259, -0.7259,  0.0   ];\n",
    "        v = [0.0,  0.0,    -0.7259, -0.7259];\n",
    "        \n",
    "    if IC == 3:\n",
    "        \n",
    "        p = [1.5, 0.3,    0.029, 0.3   ];\n",
    "        r = [1.5, 0.5323, 0.138, 0.5323];\n",
    "        u = [0.0, 1.206,  1.206, 0.0   ];\n",
    "        v = [0.0, 0.0,    1.206, 1.206 ];\n",
    "        \n",
    "    if IC == 4:\n",
    "        \n",
    "        p = [1.1, 0.35,   1.1,    0.35  ];\n",
    "        r = [1.1, 0.5065, 1.1,    0.5065];\n",
    "        u = [0.0, 0.8939, 0.8939, 0.0   ];\n",
    "        v = [0.0, 0.0,    0.8939, 0.8939];\n",
    "    \n",
    "    if IC == 5:\n",
    "        \n",
    "        p = [ 1.0,   1.0,   1.0,   1.0 ];\n",
    "        r = [ 1.0,   2.0,   1.0,   3.0 ];\n",
    "        u = [-0.75, -0.75,  0.75,  0.75];\n",
    "        v = [-0.5,   0.5,   0.5,  -0.5 ];\n",
    "        \n",
    "    if IC == 6:\n",
    "        \n",
    "        p = [ 1.0,  1.0,   1.0,   1.0 ];\n",
    "        r = [ 1.0,  2.0,   1.0,   3.0 ];\n",
    "        u = [ 0.75, 0.75, -0.75, -0.75];\n",
    "        v = [-0.5,  0.5,   0.5,  -0.5 ];\n",
    "        \n",
    "    if IC == 7:\n",
    "        \n",
    "        p = [1.0,  0.4,    0.4,  0.4   ];\n",
    "        r = [1.0,  0.5197, 0.8,  0.5197];\n",
    "        u = [0.1, -0.6259, 0.1,  0.1   ];\n",
    "        v = [0.1,  0.1,    0.1, -0.6259];\n",
    "\n",
    "    if IC ==8:\n",
    "        \n",
    "        p = [0.4 ,    1.0,    1.0,  1.0   ];\n",
    "        r = [0.5197,  1.0,    0.8,  1.0   ];\n",
    "        u = [0.1,    -0.6259, 0.1,  0.1   ];\n",
    "        v = [0.1,     0.1,    0.1, -0.6259];\n",
    "        \n",
    "    if IC ==9:\n",
    "        \n",
    "        p = [1.0,  1.0,  0.4,     0.4   ];\n",
    "        r = [1.0,  2.0,  1.039,   0.5197];\n",
    "        u = [0.0,  0.0,  0.0,     0.0   ];\n",
    "        v = [0.3, -0.3, -0.8133, -0.4259];\n",
    "        \n",
    "    if IC ==10:\n",
    "        \n",
    "        p = [1.0,    1.0,     0.3333,  0.3333];\n",
    "        r = [1.0,    0.5,     0.2281,  0.4562];\n",
    "        u = [0.0,    0.0,     0.0 ,    0.0   ];\n",
    "        v = [0.4297, 0.6076, -0.6076, -0.4297];\n",
    "        \n",
    "    if IC ==11:\n",
    "        \n",
    "        p = [1.0, 0.4,    0.4, 0.4   ];\n",
    "        r = [1.0, 0.5313, 0.8, 0.5313];\n",
    "        u = [0.1, 0.8276, 0.1, 0.1   ];\n",
    "        v = [0.0, 0.0,    0.0, 0.7276];\n",
    "        \n",
    "    if IC ==12:\n",
    "        \n",
    "        p = [0.4,    1.0,    1.0, 1.0  ];\n",
    "        r = [0.5313, 1.0 ,   0.8, 1.0  ];\n",
    "        u = [0.0,    0.7276, 0.0, 0.0  ];\n",
    "        v = [0.0,    0.0,    0.0, 0.7276];\n",
    "        \n",
    "    if IC ==13:\n",
    "        \n",
    "        p = [ 1.0, 1.0, 0.4,    0.4   ];\n",
    "        r = [ 1.0, 2.0, 1.0625, 0.5313];\n",
    "        u = [ 0.0, 0.0, 0.0 ,   0.0   ];\n",
    "        v = [-0.3, 0.3, 0.8145, 0.4276];\n",
    "        \n",
    "    if IC ==14:\n",
    "        \n",
    "        p = [ 8.0,     8.0,    2.6667, 2.6667];\n",
    "        r = [ 2.0,     1.0,    0.4736, 0.9474];\n",
    "        u = [ 0.0,     0.0,    0.0,    0.0   ];\n",
    "        v = [-0.5606, -1.2172, 1.2172, 1.1606];\n",
    "        \n",
    "    if IC ==15:\n",
    "        \n",
    "        p = [ 1.0,  0.4,     0.4, 0.4   ];\n",
    "        r = [ 1.0,  0.5197,  0.8, 0.5313];\n",
    "        u = [ 0.1, -0.6259,  0.1, 0.1   ];\n",
    "        v = [-0.3, -0.3,    -0.3, 0.4276];\n",
    "        \n",
    "    if IC ==16:\n",
    "        \n",
    "        p = [0.4,     1.0,    1.0, 1.0  ];\n",
    "        r = [0.5313,  1.0222, 0.8, 1.0  ];\n",
    "        u = [0.1,    -0.6179, 0.1, 0.1  ];\n",
    "        v = [0.1,     0.1,    0.1, 0.8276];\n",
    "        \n",
    "    if IC ==17:\n",
    "        \n",
    "        p = [ 1.0,  1.0, 0.4,     0.4   ];\n",
    "        r = [ 1.0,  2.0, 1.0625,  0.5197];\n",
    "        u = [ 0.0,  0.0, 0.0 ,    0.0   ];\n",
    "        v = [-0.4, -0.3, 0.2145, -1.1259];\n",
    "        \n",
    "    if IC ==18:\n",
    "        \n",
    "        p = [1.0,  1.0, 0.4,    0.4   ];\n",
    "        r = [1.0,  2.0, 1.0625, 0.5197];\n",
    "        u = [0.0,  0.0, 0.0,    0.0   ];\n",
    "        v = [1.0, -0.3, 0.2145, 0.2741];\n",
    "        \n",
    "    if IC ==19:\n",
    "        \n",
    "        p = [1.0,  1.0, 0.4,     0.4   ];\n",
    "        r = [1.0,  2.0, 1.0625,  0.5197];\n",
    "        u = [0.0,  0.0, 0.0,     0.0   ];\n",
    "        v = [0.3, -0.3, 0.2145, -0.4259];\n",
    "        \n",
    "    r0,u0,v0,p0 = np.zeros((len(y),len(x))),np.zeros((len(y),len(x))),np.zeros((len(y),len(x))),np.zeros((len(y),len(x)))\n",
    "\n",
    "    reg1 = np.where((x >= 0.5) & (0.5 <= y))\n",
    "    reg2 = np.where((x<0.5) & (0.5<=y))\n",
    "    reg3 = np.where((x < 0.5) & (y <0.5))\n",
    "    reg4 = np.where((x >= 0.5) & (y<=0.5))\n",
    "\n",
    "    r0[reg1], r0[reg2], r0[reg3], r0[reg4]  =  r[0], r[1], r[2], r[3]\n",
    "    u0[reg1],u0[reg2], u0[reg3], u0[reg4]  =  u[0], u[1], u[2], u[3]\n",
    "    v0[reg1], v0[reg2], v0[reg3], v0[reg4] =  v[0], v[1], v[2], v[3]\n",
    "    p0[reg1],p0[reg2], p0[reg3], p0[reg4]  =  p[0], p[1], p[2], p[3]\n",
    "\n",
    "    return r0, u0, v0, p0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "CFL     = 0.5       #CFL number\n",
    "tEnd    = 0.3       #Final time\n",
    "nx      = 150       #Number of cells/Elements in x\n",
    "ny      = 150       #Number of cells/Elements in y\n",
    "n       = 5         #Degrees of freedom: ideal air=5, monoatomic gas=3\n",
    "IC      = 3\n",
    "fluxMth ='HLLE'\t   \n",
    "limiter ='MC'      \n",
    "\n",
    "gamma = 1.4\n",
    "\n",
    "Lx = 1\n",
    "dx = Lx/nx\n",
    "xc = np.arange(dx/2,Lx, dx)\n",
    "\n",
    "Ly = 1\n",
    "dy = Ly/ny\n",
    "yc = np.arange(dy/2,Ly, dy)\n",
    "\n",
    "x,y = np.meshgrid(xc,yc)\n",
    "\n",
    "r0,u0,v0,p0 = Euler_IC(x,y,IC)\n",
    "\n",
    "\n",
    "E0 = p0/((gamma-1)*r0)+0.5*(u0**2 + v0**2)\n",
    "c0 = np.sqrt(gamma*p0/r0)\n",
    "Q0 = np.zeros((ny,nx,4))\n",
    "\n",
    "Q0[:,:,0] = r0\n",
    "Q0[:,:,1] = r0*u0\n",
    "Q0[:,:,2] =  r0*v0\n",
    "Q0[:,:,3] = r0*E0\n",
    "\n",
    "nx += 2\n",
    "ny += 2\n",
    "\n",
    "q0 = np.zeros((ny,nx,4))\n",
    "q0[1:ny-1,1:nx-1,0:4] = Q0\n",
    "\n",
    "q0[:,0,:] = q0[:,1,:]\n",
    "q0[:,nx-1,:] = q0[:,nx-2,:]\n",
    "\n",
    "q0[0,:,:] = q0[1,:,:]\n",
    "q0[ny-1,:,:] = q0[ny-1,:,:]\n",
    "\n",
    "vn = np.sqrt(u0**2+v0**2)\n",
    "lambda1=vn+c0\n",
    "lambda2=vn-c0\n",
    "a0  = np.max(np.abs([lambda1, lambda2]))\n",
    "dt0 = 0.0011\n",
    "\n",
    "q=q0\n",
    "t=dt0\n",
    "it=0\n",
    "dt=0.0011\n",
    "a=a0\n",
    "\n",
    "Y = y.flatten()[:, None]                                          # Vectorized y_grid\n",
    "X = x.flatten()[:, None]                                          # Vectorized x_grid\n",
    "\n",
    "# Sample grid (Batch training)\n",
    "num_f_train = 11000\n",
    "id_f = np.random.choice((nx-2)*(ny-2), num_f_train, replace=False) \n",
    "\n",
    "\n",
    "X_test = X[:, 0][:, None]                                            \n",
    "Y_test = Y[:, 0][:, None]                                            \n",
    "X_int = X[:, 0][:, None]                                            \n",
    "Y_int = Y[:, 0][:, None]                                               \n",
    "\n",
    "x_int_train = np.hstack((X_int, Y_int))                              \n",
    "x_test = np.hstack((X_test, Y_test))\n",
    "\n",
    "x_int_train = torch.tensor(x_int_train, requires_grad=True, dtype=torch.float32).to(device)\n",
    "x_test = torch.tensor(x_test, requires_grad=True, dtype=torch.float32).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GbsCB+zjJj2qt/0vz+FdJvkySUsr3M1sO6Wbenql61ULLD1rKph3V3RYO22YRHwP9DAAA9E2G3Y1s9YZ+BgCAK8wA8MUmSV60/aLW+tskXzQ/KaV8kllwfravxnVk37N6Jx3VfdXoZwAAoG+TyLC7mLaUTTqq+6rRzwAAcIUZAL7YeZKTJP/xsgNrrb/KbHb1VXOy7xOWUk5qrW17/gyilDLJbAb8zcz646PMwud58/M8yVe11ukOpzn6fgYAAHonw/bg0LKVDAsAAFzGAPDFXmQWqH49dEP2bNpz/SeZhdJBlVJOMtsb686KQxYD76NSylmS+7XW1hn1W5h2VM8qB9HPAADA3siw/TiIbCXDAgAA6/re0A04cP8sq4PVldeExz79bkX5pOfzXqqUci/Jy7x5fx9n9kHJtVprSfJh8/j+wtNOkzwvpTza8FxH288AAMBeybC7OdhsJcMCAACb8A3gC9RaX5RSXpRS/mWt9R8P3Z4eTIZuwECeZPbap0l+Vmt9uHxAs1TWWZKzUsrj5jmnza/vlFI+TvLJmktqTXZu8Qh822PdH/RYNwDA/n3TU7193pFxCGTY0ZJhByHFAgCsR4Y9RAaAL3c7sxmz/7rW+t8M3ZiR+Gjg808yW1bq5jr7CzUB+WYp5XmS603x9cyuixs77qvUp6H7+S0/77Huz3usGwBg/74YugFcbTJs94bOVpPIsAOQYgEA1iPDHiIDwJeotU5LKbcyC0r/a2YzZv/3odvVkQMLV717lTfBeZvQezuzJbfmTjKbVX3zkucdWz8DAAADkWFHRYYdq//vnw/dAgCA7vxfPh+6BbQwAHyJUso/z2yPnZrk4yTTUsqDJF/VWv/dkG3r0aue65/0XP8qL5o/t5rxXGs9b5bSWtxT67SUcqfW+niL9oy1nwEAgIHIsL2Y9Fz/KjLsSNV/X4ZuAgBAZ9zZHCYDwBcopfw4yd2FoprZtXw/yf1SSpI8TfIsszD9t3tvZD9GOdu31nq7g2oe5e3wnCQPkmwTnkfZzwAAwDBk2HGRYcfr//l/HboFAACMnQHgi91NMk3yVWZB53pmSyYtTmi41fw8KqW8THK71vr/23M7t9X37N1VpgOdd2e11hellGnenpk8KaXcq7U+XPE0/ZzkJ0neH7oRAABXwk97qvfb9LunJQdAhu3HdKDz7kyG3Z4MCwCwHgn2MBkAvtj1JKe11l/PC0op309ymtmeOad5O0T9cZJfJfk/7LGN7N/Xmb33i+4mWRWeySw4fzB0IwAArgR3TWxNhqWNDLsFGRYAYD3umQ7T94ZuwIE7T/J7iwW11t/WWr+stX5aa/0oybXMltOa783zIlfHdKDzDjWbuCvnLWUnpZTJiuOn/TXlQle9nwEAgM3IsP246tlKhgUAgCNjAPhiD5M8LaX8k1UHNGH6i1rrx7XW79Va/2SP7dvVqnA16aj+319RPu2o/kPz6Ypy/QwAAOyDDLubY8tWMiwAAIyUAeAL1FofJ/lhki9LKb8rpfxPpZS/N3S7ulJrnQ506rbZx1fJdEX5pK1QPwMAAPsgw/bmqmer6YrySVuhfgYAgKvPAPAlaq1PM1si618k+cdJPh62RZ2btpR91FHdkxXle13WqZRyWkp5UEq5d8ESV5v43Yryaxc8Z9pSNqp+BgAAhifD7mSyolyGnRlVPwMAwJgZAF5Ds0TW/VrrR7XWXw/dno61zbCddFR3azjc52ziUsqjJM+S3EvyIMnrUsr1nk53URgedT8DAACHQ4bd2uDZSoYFAAC6YACYs5ayPmf1tp2vF6WUkyR3Wn71pKdTXrRc1Wj7GQAAYI9Gm61kWAAAoCsGgHnWUjbpqO6TlrIXHdW9jtMV5W3t2sTvryh/ecFzxtzPAAAA+zLmbCXDAgAAnTAAzNdthc3M41211fGbDuod2qq+uWj2tH4GAADYnWy1ORkWAACOjAHgI9fssdMW+nYKdaWUyYrzPd2l3g29WlH+eMd62/pmWmtduWTVyPsZAABgL0aerWRYAACgEwaASZJHLWXXd6zz45ayXUPrppaXkJomeVhrvbtjvW1989UazxtrPwMAAOzTWLOVDAsAAHTi6AaASyk/O+bzr9AWtm7uWOftlrK28HihUsqklHJaStk4ZNZaz/NmxvLdWuuHtdb7m9az1J5VezKt89oOtp8BAIDDNHSGHPr8KxxstpJhLyXDAgDAHhzdAHCSG6WUfznEiUspv8zus2U71yzttBzsVoXEdS0//0WtdXk284VKKfeSvE7yLMnzUsrLLfYbmgfJBxs+b5W2mdeP13lth9rP+/Jtkm/W/AEAYFPr3GV9O1jr2IkMu+RQs5UMuxYZFgAACXYPSq116DbsVbPfzHmSf1Nr/cd7PO9XST5J8v1a69/u67zravrl9VLx/Vrrwy3qupXkyVLxjU1C3Yo6kuS81nptw/a8TjLJbOmsrWdPN8H95VLxNLP3dLpmHZMcUD/3pZTyB0n+atvnf95dUwA68ec/Sv7iyz/Jn/76l8knL5L85dBNAljy+bZP/MNa61932BA6JsO2O7RsJcOuVZcMCwBAkp3un2TYNR3dN4CbkHOa5NNSyv9aSvm7fZ6vlPL3Sim/SfKPknxyiME5+a5floPlZ03Y29TyTOW1ZhcvWbXH0ckFS1it8uPmz3ullDsbPndRW5j/ZN3gnBxkPwMAAAdMhm13gNlKhr2cDAsAAHtydAPASdIEjD9J8nGS35ZS/kkf5yml/CjJb5PcSPIntdZ/18d5utLM4H26UDRJ8qtN6iilPEqyuMTVWa11VRC+yEXLZG20hFat9WmS+ezkR82yXBsppTzJu0uf3d4mrB5YPwMAAAdOhm13YNlKhr24PTIsAADs0VEOACdJrfUss/D8d5I8bmZS/7dd1F1K+e+aGdOPkpTMljTaKBwNpdZ6O28Hu+ullOfrzO5tAt3i7OQXtdabWzblolD69aaVNctmzfcuelBKeVZKuXQvq1LKSSnleZJbC8XTzN7Tp+3PWqs9h9LPAADAFSDDtjugbCXDrm6TDAsAAHt2dHsALyul/F5mIeaTJDWzYPSLJE9rrb9es46/l9mSXDeTfJrZbNiS5CyzGbZ/03nDe9bMMF5enulhkke11vOF4yaZveYHmb3uuce7zOZdsVdRMntfbu9Q753MPtSYO8tsWayvM9ubadqc+3pmS3gtL9U1f0+n27ZhqT2D9nNf2vZP+kmS99d8/gedtwhgN/YABg7fN2sc822Sny8X2j/pipFh2w2drWRYGRYAgPVtnWBl2LUd/QDwXCnlVmbB5PuZhei58+ZnmuRV8+ckyUfNnyd5exmj0hx/v9b6r/ptdb+aEPkgb88cnpvm7RA3d5bZa995H5/m/I8yC7DnmQXn5b2Htql3kuTLtL+uVTp7XS3tGbSf+9AWnn8aoRi4ugwAA+PwTZIvlguF5ytKhn3X0NlKhk0iwwIA0JHWBCvDru29oRtwKJqlkJ42IfqzJP+n5lfXcvF+PWXh7y+S/Oyqh+a5Zvbu7eS7Dxd+mDcfFkzy5gOF8yTPMgu352117XD+zpeFamY+316YkXwzs9nS8w9EpnnzockvMtubaNp1OxbaM2g/AwAAV48M+66hs5UMK8MCAMChMAC8ZCFE/15ms3bnQWaSt0P0fEb1YsC6cstkrWveL0O3o0tNIH6cN/sqDW6M/QwAAPRHhm03xmwlwwIAAOsyALxCE4T/VfMDAAAAB0uGBQAAYO57QzcAAAAAAAAAgG4YAAYAAAAAAAAYCQPAAAAAAAAAACNhABgAAAAAAABgJAwAAwAAAAAAAIyEAWAAAAAAAACAkTAADAAAAAAAADASBoAvUEr5R6WU/1xK+Q+llH9eSvmvh24TAAAAtJFhAQAASAwAX+YHSUqSkyR3kjxrwvRvSin/fSnlHwzaOgAAAHhDhgUAACDvDd2AA/ezJNeS/KOl8uvNT0op0yRnSZ4lOau1/sc9tg+ujG83OPaD3loBADBW36xxzCZ3ZFxRMix0RIYFAOiPBNs/A8AXqLX+TZLbpZTfS3Ka5Gbz58nCYZMkt5qflFLOk/zPSb6stf7tXhsMB+znGxz7eV+NAAAYrS+GbgAHQIaF7siwAAD9kWD7ZwnoNdRa/6bW+q9qrX9Wa/3jzGZU303yNLPltRZ/TpI8THJeSvmHQ7UZAACA4yTDAgAAHDcDwFuotf621vplrfXTWuv3kvzT5lev8yZEf5TkrJTyXw3VTgAAAJBhAQAAjosB4A7UWh9mFqBfZTZ7+s+SnGcWon9VSvmj4VoHAAAAb8iwAAAA42YP4I7UWh+WUj5N8o9qrf9jksellEdJfpzknyX5x4M2EAb2kyTvD90IAIDR+ukax3ybzXa1ZMxkWLiYDAsA0B8Jtn8GgLv1OLOw/D8mSa31biklSX5USrlTa/3bIRsHQ3o/yQdDNwIAYLTcabEVGRZWkGEBAPrjPqt/loDu1rMk1xcLaq13k/w2b/ZYAgAAgEMgwwIAAIyQAeBLlFL++1LKfyil/LKU8k8uOXyyovxRkhvdtgwAAADeJsMCAABgAPgCpZQfJ3mQ5CTJaWZ7Iv3nUsr/VEr5By1P+SzJeUv5WVMHAAAA9EKGBQAAILEH8GXuJilJpnl7ZvTdJHdLKdPMgnEyWzbrJMnDlnqmEZ4BAADolwwLAACAbwBf4nqS01rrR0muJfkisyBcmp9JklvNz7Xmdz9LklLKHzVLb/1Rc9x0nw0HAADg6MiwAAAAGAC+xDRJTZJa629rrfebIP1pZrOmy8LPeZKPa61/2zz3LLOlt142xz/fb9MBAAA4MtPIsAAAAEfPEtAX+zKz5bB+sFhYa32a5GmSlFK+n+RVrfVvlp47D9VJci/J7X6bCgAAwJGTYQEAAPAN4IvUWu8n+aiU8q9LKX93xTG/bQnOyWxJrX+b2Qzsf1pr/V/6aykAAADHToYFAAAg8Q3gddzIbOmraSnlQa31/7HOk2qt/7Z5LgA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      "text/plain": [
       "<Figure size 1980x1500 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(['science','ieee']):\n",
    "    fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows=2, ncols=2)\n",
    "    ax1.contourf(x, y, r0,100, cmap='jet',extend='both')\n",
    "    ax2.contourf(x, y, p0,100,cmap='jet',extend='both')\n",
    "    ax3.contourf(x, y, u0,100,cmap='jet',extend='both')\n",
    "    ax4.contourf(x, y, v0,100,cmap='jet',extend='both')\n",
    "\n",
    "\n",
    "    ax1.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax1.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax1.set_title(r'$\\rho(x,y,0)$', fontsize=7)\n",
    "\n",
    "    ax2.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax2.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax2.set_title(r'$p(x,y,0)$', fontsize=7)\n",
    "\n",
    "    ax3.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax3.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax3.set_title('$u(x,y,0)$', fontsize=7)\n",
    "\n",
    "    ax4.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax4.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax4.set_title(r'$v(x,y,0)$', fontsize=7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".....................................\n",
      "Time iteration: 0\n",
      "Start training t = 0.001100\n",
      "Total loss: 0.00007326\n",
      "Total training time (secs): 84.474192\n",
      ".....................................\n",
      "Time iteration: 1\n",
      "Start training t = 0.002200\n",
      "Total loss: 0.00007178\n",
      "Total training time (secs): 81.558573\n",
      ".....................................\n",
      "Time iteration: 2\n",
      "Start training t = 0.003300\n",
      "Total loss: 0.00007829\n",
      "Total training time (secs): 85.818507\n",
      ".....................................\n",
      "Time iteration: 3\n",
      "Start training t = 0.004400\n",
      "Total loss: 0.00007552\n",
      "Total training time (secs): 86.071660\n",
      ".....................................\n",
      "Time iteration: 4\n",
      "Start training t = 0.005500\n",
      "Total loss: 0.00006953\n",
      "Total training time (secs): 86.803466\n",
      ".....................................\n",
      "Time iteration: 5\n",
      "Start training t = 0.006600\n",
      "Total loss: 0.00007804\n",
      "Total training time (secs): 91.246320\n",
      ".....................................\n",
      "Time iteration: 6\n",
      "Start training t = 0.007700\n",
      "Total loss: 0.00006868\n",
      "Total training time (secs): 96.278622\n",
      ".....................................\n",
      "Time iteration: 7\n",
      "Start training t = 0.008800\n",
      "Total loss: 0.00006961\n",
      "Total training time (secs): 87.131264\n",
      ".....................................\n",
      "Time iteration: 8\n",
      "Start training t = 0.009900\n",
      "Total loss: 0.00009301\n",
      "Total training time (secs): 93.761454\n",
      ".....................................\n",
      "Time iteration: 9\n",
      "Start training t = 0.011000\n",
      "Total loss: 0.00008804\n",
      "Total training time (secs): 100.967691\n",
      ".....................................\n",
      "Time iteration: 10\n",
      "Start training t = 0.012100\n",
      "Total loss: 0.00007928\n",
      "Total training time (secs): 108.343374\n",
      ".....................................\n",
      "Time iteration: 11\n",
      "Start training t = 0.013200\n",
      "Total loss: 0.00009014\n",
      "Total training time (secs): 102.186162\n",
      ".....................................\n",
      "Time iteration: 12\n",
      "Start training t = 0.014300\n",
      "Total loss: 0.00007355\n",
      "Total training time (secs): 93.972742\n",
      ".....................................\n",
      "Time iteration: 13\n",
      "Start training t = 0.015400\n",
      "Total loss: 0.00007543\n",
      "Total training time (secs): 84.453406\n",
      ".....................................\n",
      "Time iteration: 14\n",
      "Start training t = 0.016500\n",
      "Total loss: 0.00008946\n",
      "Total training time (secs): 90.649750\n",
      ".....................................\n",
      "Time iteration: 15\n",
      "Start training t = 0.017600\n",
      "Total loss: 0.00007867\n",
      "Total training time (secs): 100.303566\n",
      ".....................................\n",
      "Time iteration: 16\n",
      "Start training t = 0.018700\n",
      "Total loss: 0.00007763\n",
      "Total training time (secs): 97.557435\n",
      ".....................................\n",
      "Time iteration: 17\n",
      "Start training t = 0.019800\n",
      "Total loss: 0.00009263\n",
      "Total training time (secs): 99.648565\n",
      ".....................................\n",
      "Time iteration: 18\n",
      "Start training t = 0.020900\n",
      "Total loss: 0.00008157\n",
      "Total training time (secs): 88.309117\n",
      ".....................................\n",
      "Time iteration: 19\n",
      "Start training t = 0.022000\n",
      "Total loss: 0.00007644\n",
      "Total training time (secs): 86.915059\n",
      ".....................................\n",
      "Time iteration: 20\n",
      "Start training t = 0.023100\n",
      "Total loss: 0.00008924\n",
      "Total training time (secs): 80.208587\n",
      ".....................................\n",
      "Time iteration: 21\n",
      "Start training t = 0.024200\n",
      "Total loss: 0.00007156\n",
      "Total training time (secs): 80.393862\n",
      ".....................................\n",
      "Time iteration: 22\n",
      "Start training t = 0.025300\n",
      "Total loss: 0.00006991\n",
      "Total training time (secs): 83.056180\n",
      ".....................................\n",
      "Time iteration: 23\n",
      "Start training t = 0.026400\n",
      "Total loss: 0.00007055\n",
      "Total training time (secs): 85.581591\n",
      ".....................................\n",
      "Time iteration: 24\n",
      "Start training t = 0.027500\n",
      "Total loss: 0.00008660\n",
      "Total training time (secs): 85.971822\n",
      ".....................................\n",
      "Time iteration: 25\n",
      "Start training t = 0.028600\n",
      "Total loss: 0.00007640\n",
      "Total training time (secs): 87.081326\n",
      ".....................................\n",
      "Time iteration: 26\n",
      "Start training t = 0.029700\n",
      "Total loss: 0.00008601\n",
      "Total training time (secs): 90.166575\n",
      ".....................................\n",
      "Time iteration: 27\n",
      "Start training t = 0.030800\n",
      "Total loss: 0.00008662\n",
      "Total training time (secs): 87.299304\n",
      ".....................................\n",
      "Time iteration: 28\n",
      "Start training t = 0.031900\n",
      "Total loss: 0.00007467\n",
      "Total training time (secs): 90.024270\n",
      ".....................................\n",
      "Time iteration: 29\n",
      "Start training t = 0.033000\n",
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      "Start training t = 0.292600\n",
      "Total loss: 0.00017513\n",
      "Total training time (secs): 80.061743\n",
      ".....................................\n",
      "Time iteration: 266\n",
      "Start training t = 0.293700\n",
      "Total loss: 0.00015076\n",
      "Total training time (secs): 80.112316\n",
      ".....................................\n",
      "Time iteration: 267\n",
      "Start training t = 0.294800\n",
      "Total loss: 0.00017201\n",
      "Total training time (secs): 83.060200\n",
      ".....................................\n",
      "Time iteration: 268\n",
      "Start training t = 0.295900\n",
      "Total loss: 0.00015226\n",
      "Total training time (secs): 80.158310\n",
      ".....................................\n",
      "Time iteration: 269\n",
      "Start training t = 0.297000\n",
      "Total loss: 0.00017461\n",
      "Total training time (secs): 80.025696\n",
      ".....................................\n",
      "Time iteration: 270\n",
      "Start training t = 0.298100\n",
      "Total loss: 0.00018640\n",
      "Total training time (secs): 80.043874\n",
      ".....................................\n",
      "Time iteration: 271\n",
      "Start training t = 0.299200\n",
      "Total loss: 0.00021340\n",
      "Total training time (secs): 80.557540\n",
      ".....................................\n",
      "Total training time (secs):  25683.476345539093\n"
     ]
    }
   ],
   "source": [
    "tic_total = time.time()\n",
    "while (t < tEnd):\n",
    "    DF = autodiff()\n",
    "    DF_nn = DF.MUSCL_Euler_2D(q, a, gamma, dx, dy, nx, ny, limiter, fluxMth)\n",
    "    f = q - dt*DF_nn\n",
    "\n",
    "    print('.....................................')\n",
    "    print('Time iteration: %d' % it)\n",
    "    print('Start training t = %f' % t)\n",
    "      \n",
    "          \n",
    "    model = DNN().to(device)\n",
    "    # Loss and optimizer\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)\n",
    "    # Train PINNs\n",
    "    def train(epoch):\n",
    "        model.train()\n",
    "        def closure():\n",
    "            optimizer.zero_grad()\n",
    "            l1_norm = sum(torch.linalg.norm(p, 1) for p in model.parameters())\n",
    "            loss_FVM = model.loss_FVM(x_int_train, f, nx,ny,id_f) + 1e-6*l1_norm\n",
    "            loss = loss_FVM\n",
    "            loss.backward()\n",
    "            return loss\n",
    "\n",
    "        # Optimize loss function\n",
    "        loss = optimizer.step(closure)\n",
    "        loss_value = loss.item() if not isinstance(loss, float) else loss\n",
    "        \n",
    "        #if epoch % 10000 == 0:\n",
    "          # print(f'Epoch {epoch}: Loss: {loss_value:.8f}')\n",
    "            \n",
    "        return loss_value\n",
    "        \n",
    "    tic = time.time()\n",
    "    #epoch = 0\n",
    "    epochs= 20000\n",
    "    #loss_value = np.inf\n",
    "\n",
    "    for epoch in range(0,epochs+1):\n",
    "        loss_value = train(epoch)\n",
    "        #epoch += 1\n",
    "                       \n",
    "    toc = time.time()\n",
    "    total_time = toc - tic\n",
    "    print(f'Total loss: {loss_value:.8f}')\n",
    "    print('Total training time (secs): %f' % (total_time))\n",
    "        \n",
    "    q_nn = to_numpy(model(x_test))  \n",
    "    q_nn = pd.DataFrame(q_nn).interpolate()\n",
    "    q_nn = np.reshape(q_nn.to_numpy(),(ny-2,nx-2,4))\n",
    "            \n",
    "    q = np.zeros((ny, nx,4))\n",
    "    q[1:ny - 1, 1:nx - 1,:] = q_nn\n",
    "    \n",
    "    q[:, :, 1] = q[:, :, 1]*q[:, :, 0]\n",
    "    q[:, :, 2] = q[:, :, 2]*q[:, :, 0]\n",
    "    q[:, :, 3] = q[:, :, 3]*q[:, :, 0]\n",
    "    \n",
    "    q[:, 0,:]=q[:, 1,:]\n",
    "    q[:, nx-1,:]=q[:, nx - 2,:]\n",
    "\n",
    "    q[0,:,:] = q[1,:,:]\n",
    "    q[ny-1,:,:] = q[ny - 2,:,:]\n",
    "            \n",
    "    r = q[:, :, 0]\n",
    "    u = q[:, :, 1]/r\n",
    "    v = q[:, :, 2]/r\n",
    "    E = q[:, :, 3]/r\n",
    "        \n",
    "    p = (gamma - 1) * r * (E - 0.5 * (u ** 2 + v ** 2))\n",
    "    c = np.sqrt(np.abs(gamma * p / r))\n",
    "\n",
    "    vn = np.sqrt(np.abs(u ** 2 + v ** 2));\n",
    "    lambda1 = vn + c\n",
    "    lambda2 = vn - c\n",
    "    a = np.max(np.abs([lambda1, lambda2]))\n",
    "\n",
    "    t = t + dt\n",
    "    it = it + 1 \n",
    "       \n",
    "tic_total_final = time.time() - tic_total\n",
    "print('.....................................')\n",
    "print('Total training time (secs): ', tic_total_final)\n",
    "q=q[1:ny-1,1:nx-1,0:4]\n",
    "r=q[:,:,0]\n",
    "u=q[:,:,1]/r\n",
    "v=q[:,:,2]/r\n",
    "E=q[:,:,3]/r\n",
    "p=(gamma-1)*r*(E-0.5*(u**2+v**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1980x1500 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(['science','ieee']):\n",
    "    fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows=2, ncols=2)\n",
    "    ax1.contourf(x, y, r, 100,cmap='jet')\n",
    "    ax2.contourf(x, y, p, 100,cmap='jet',extend='both')\n",
    "    ax3.contourf(x, y, u, 100,cmap='jet',extend='both')\n",
    "    ax4.contourf(x, y, v, 100,cmap='jet',extend='both')\n",
    "\n",
    "\n",
    "    ax1.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax1.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax1.set_title(r'$\\rho(x,y,0.3)$', fontsize=5)\n",
    "\n",
    "    ax2.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax2.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax2.set_title(r'$p(x,y,0.3)$', fontsize=5)\n",
    "\n",
    "    ax3.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax3.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax3.set_title(r'$u(x,y,0.3)$', fontsize=5)\n",
    "\n",
    "    ax4.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax4.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax4.set_title(r'$v(x,y,0.3)$', fontsize=5)\n",
    "fig.suptitle(r'FV-PINNs - 2D Shocktube (Liska, 2003)', fontsize=7)\n",
    "plt.tight_layout()\n",
    "plt.savefig('2-D.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test on refined grid \n",
    "nx_new      = 240       #Number of cells/Elements in x\n",
    "ny_new      = 240       #Number of cells/Elements in y\n",
    "\n",
    "dx_new = Lx/nx_new\n",
    "xc_new = np.arange(dx_new/2,Lx, dx_new)\n",
    "\n",
    "dy_new = Ly/ny_new\n",
    "yc_new = np.arange(dy_new/2,Ly, dy_new)\n",
    "\n",
    "x_new,y_new = np.meshgrid(xc_new,yc_new)\n",
    "Y_new = y_new.flatten()[:, None]                                          # Vectorized y_grid\n",
    "X_new = x_new.flatten()[:, None]                                          # Vectorized x_grid\n",
    "\n",
    "X_test_new = X_new[:, 0][:, None]                                         # Random x - interior\n",
    "Y_test_new = Y_new[:, 0][:, None]                                         # Random Y - interior\n",
    "\n",
    "x_test_new = np.hstack((X_test_new, Y_test_new))\n",
    "x_test_new = torch.tensor(x_test_new, requires_grad=True, dtype=torch.float32).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_nn = to_numpy(model(x_test_new))  \n",
    "q_nn = pd.DataFrame(q_nn).interpolate()\n",
    "q_nn = np.reshape(q_nn.to_numpy(),(ny_new,nx_new,4))\n",
    "            \n",
    "q = np.zeros((ny_new-2, nx_new-2,4))\n",
    "q = q_nn\n",
    "    \n",
    "q[:, :, 1] = q[:, :, 1]*q[:, :, 0]\n",
    "q[:, :, 2] = q[:, :, 2]*q[:, :, 0]\n",
    "q[:, :, 3] = q[:, :, 3]*q[:, :, 0]\n",
    "    \n",
    "q[:, 0,:]=q[:, 1,:]\n",
    "q[:, nx-1,:]=q[:, nx - 2,:]\n",
    "\n",
    "q[0,:,:] = q[1,:,:]\n",
    "q[ny-1,:,:] = q[ny - 2,:,:]\n",
    "            \n",
    "r = q[:, :, 0]\n",
    "u = q[:, :, 1]/r\n",
    "v = q[:, :, 2]/r\n",
    "E = q[:, :, 3]/r\n",
    "        \n",
    "p = (gamma - 1) * r * (E - 0.5 * (u ** 2 + v ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1980x1500 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(['science','ieee']):\n",
    "    fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows=2, ncols=2)\n",
    "    ax1.contourf(x_new, y_new, r, 1000,cmap='jet')\n",
    "    ax2.contourf(x_new, y_new, p, 1000,cmap='jet',extend='both')\n",
    "    ax3.contourf(x_new, y_new, u, 1000,cmap='jet',extend='both')\n",
    "    ax4.contourf(x_new, y_new, v, 1000,cmap='jet',extend='both')\n",
    "\n",
    "\n",
    "    ax1.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax1.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax1.set_title(r'$\\rho(x,y,0.3)$', fontsize=5)\n",
    "\n",
    "    ax2.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax2.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax2.set_title(r'$p(x,y,0.3)$', fontsize=5)\n",
    "\n",
    "    ax3.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax3.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax3.set_title(r'$u(x,y,0.3)$', fontsize=5)\n",
    "\n",
    "    ax4.set_xlabel(r'$x(m)$', fontsize=4)\n",
    "    ax4.set_ylabel(r'$y(m)$', fontsize=4)\n",
    "    ax4.set_title(r'$v(x,y,0.3)$', fontsize=5)\n",
    "fig.suptitle(r'FV-PINNs - 2D Shocktube (Liska, 2003)', fontsize=7)\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
